Distributed gene expression modelling for exploring variability in epigenetic function.

ABSTRACT:

Background

Predictive gene expression modelling is an important tool in computational biology due to the volume of high-throughput sequencing data generated by recent consortia. However, the scope of previous studies has been restricted to a small set of cell-lines or experimental conditions due an inability to leverage distributed processing architectures for large, sharded data-sets.

Results

We present a distributed implementation of gene expression modelling using the MapReduce paradigm and prove that performance improves as a linear function of available processor cores. We then leverage the computational efficiency of this framework to explore the variability of epigenetic function across fifty histone modification data-sets from variety of cancerous and non-cancerous c

SUBMITTER: Budden DM 

PROVIDER: S-EPMC5097851 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature